Top

Empirical Software Engineering

Published in:

01-03-2023

What makes Ethereum blockchain transactions be processed fast or slow? An empirical study

Authors: Michael Pacheco, Gustavo A. Oliva, Gopi Krishnan Rajbahadur, Ahmed E. Hassan

Published in: Empirical Software Engineering | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The Ethereum platform allows developers to implement and deploy applications called ÐApps onto the blockchain for public use through the use of smart contracts. To execute code within a smart contract, a paid transaction must be issued towards one of the functions that are exposed in the interface of a contract. However, such a transaction is only processed once one of the miners in the peer-to-peer network selects it, adds it to a block, and appends that block to the blockchain This creates a delay between transaction submission and code execution. It is crucial for ÐApp developers to be able to precisely estimate when transactions will be processed, since this allows them to define and provide a certain Quality of Service (QoS) level (e.g., 95% of the transactions processed within 1 minute). However, the impact that different factors have on these times have not yet been studied. Processing time estimation services are used by ÐApp developers to achieve predefined QoS. Yet, these services offer minimal insights into what factors impact processing times. Considering the vast amount of data that surrounds the Ethereum blockchain, changes in processing times are hard for ÐApp developers to predict, making it difficult to maintain said QoS. In our study, we build random forest models to understand the factors that are associated with transaction processing times. We engineer several features that capture blockchain internal factors, as well as gas pricing behaviors of transaction issuers. By interpreting our models, we conclude that features surrounding gas pricing behaviors are very strongly associated with transaction processing times. Based on our empirical results, we provide ÐApp developers with concrete insights that can help them provide and maintain high levels of QoS.

previous article An empirical study of text-based machine learning models for vulnerability detection

next article Applying declarative analysis to industrial automotive software product line models

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

https://etherscan.io/gastracker

https://ethgasstation.info

The transaction logs can be seen at https://etherscan.io/tx/0x5641c38bf30fd19ad3332100762017573ee676f35250dcf4a976bb0cfe31ac2fhttps://etherscan.io/tx/0x5641c38bf30fd19ad3332100762017573ee676f35250dcf4a976bb0cfe31ac2f

https://www.myetherwallet.com/

https://etherscan.io/

https://cloud.google.com/bigquery

The dataset is bigquery-public-data.crypto_ethereum

https://etherscan.io/chart/pendingtx

https://etherscan.io/chart/networkutilization

https://www.rdocumentation.org/packages/Hmisc/versions/4.5-0/topics/varclus

https://www.rdocumentation.org/packages/Hmisc/versions/4.5-0/topics/redun

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

https://shap.readthedocs.io/en/latest/index.html

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html

https://etherchain.org/tools/gasPriceOracle

https://github.com/SAILResearch/suppmaterial-20-mpacheco-ethereum_tx_time_interpretation

Aniche M, Maziero E, Durelli R, Durelli V (2020) The effectiveness of supervised machine learning algorithms in predicting software refactoring. IEEE Transactions on Software Engineering

Ante L (2021) How elon musk’s twitter activity moves cryptocurrency markets. Advertising & Marketing Law eJournal

Bao L, Xia X, Lo D, Murphy GC (2021) A large scale study of long-time contributor prediction for github projects. IEEE Trans Softw Eng 47 (6):1277–1298. https://doi.org/10.1109/TSE.2019.2918536CrossRef

BBC (2017) Bbc news: Cryptokitties craze slows down transactions on ethereum. https://www.bbc.com/news/technology-42237162, [Online; accessed 02-November-2022]

Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(2):281–305MathSciNetMATH

Binder M (2022) Bored ape yacht club caused ethereum fees to soar to astronomical levels. https://mashable.com/article/ethereum-gas-fees-skyrocket-bored-ape-yacht-club-otherside-nft-launch, [Online; accessed 10-May-2022]

Bird C, Nagappan N, Murphy B, Gall H, Devanbu P (2011) Don’t touch my code! examining the effects of ownership on software quality

Boslaugh S, Watters PA (2008) Statistics in a nutshell - a desktop quick reference

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefMATH

Buterin V (2014) Ethereum: A next-generation smart contract and decentralized application platform. https://github.com/ethereum/wiki/wiki/White-Paper, [Online; accessed 20-November-2019]

Chen T, Li X, Luo X, Zhang X (2017) Under-optimized smart contracts devour your money. In: 2017 IEEE 24Th international conference on software analysis, evolution and reengineering, SANER, IEEE, pp 442-446

Comben C (2018) What are blockchain confirmations and why do they matter? https://coincentral.com/blockchain-confirmations, [Online; accessed 04-December-2019]

de Azevedo Sousa JE, Oliveira V, Valadares J, Dias Gonçalves G, Moraes Villela S, Soares Bernardino H, Borges Vieira A (2021) An analysis of the fees and pending time correlation in ethereum. International Journal of Network Management 31(3), https://doi.org/10.1002/nem.2113

Esteves G, Figueiredo E, Veloso A, Viggiato M, Ziviani N (2020) Understanding machine learning software defect predictions. Autom Softw Eng 27(3):369–392CrossRef

Fan Y, Xia X, Lo D, Hassan AE (2020) Chaff from the wheat: Characterizing and determining valid bug reports. IEEE Trans Softw Eng 46(05):495–525. https://doi.org/10.1109/TSE.2018.2864217CrossRef

Graham B, Zweig J (2003) The intelligent investor: Revised 1972 Ed. HarperCollins

Harrell F (2015) Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, 2nd edn. Springer

Jakobsson M, Juels A (1999) Proofs of work and bread pudding protocols. In: Proceedings of the IFIP TC6/TC11 Joint Working Conference on Secure Information Networks: Communications and Multimedia Security, Kluwer, B.V., Deventer, The Netherlands, The Netherlands, CMS ’99, pp 258–272

Jiarpakdee J, Tantithamthavorn CK, Grundy J (2021) Practitioners’ perceptions of the goals and visual explanations of defect prediction models. In: 2021 IEEE/ACM 18Th international conference on mining software repositories, MSR, IEEE, pp 432-443

Kaiser L (2019) Seasonality in cryptocurrencies. Finance Research Letters, 31

Kasahara S, Kawahara J (2019) Effect of bitcoin fee on transaction-confirmation process. Journal of Industrial & Management Optimization 15(1547-5816_2019_1_365):365, https://doi.org/10.3934/jimo.2018047

Kondo M, Oliva GA, Jiang ZMJ, Hassan AE, Mizuno O (2020) Code cloning in smart contracts: a case study on verified contracts from the ethereum blockchain platform. Empir Softw Eng 25(6):4617–4675CrossRef

Lee D, Rajbahadur GK, Lin D, Sayagh M, Bezemer CP, Hassan AE (2020) An empirical study of the characteristics of popular minecraft mods. Empir Softw Eng 25(5):3396–3429CrossRef

Liaw A (2010) [r] random forest auc. https://stat.ethz.ch/pipermail/r-help/2010-October/257208.html, [Online; accessed 17-November-2021]

Liu Y, Lu Y, Nayak K, Zhang F, Zhang L, Zhao Y (2022) Empirical analysis of eip-1559: Transaction fees, waiting time, and consensus security. arXiv:220105574

Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA, NIPS’17, pp 4768–4777

Lundberg SM, Erion GG, Lee SI (2018) Consistent individualized feature attribution for tree ensembles. arXiv:180203888

Lyu Y, Rajbahadur GK, Lin D, Chen B, Jiang ZM (2021) Towards a consistent interpretation of aiops models. ACM Trans Soft Eng Methodol (TOSEM) 31(1):1–38

Marchesi L, Marchesi M, Destefanis G, Barabino G, Tigano D (2020) Design patterns for gas optimization in ethereum. In: 2020 IEEE International workshop on blockchain oriented software engineering (IWBOSE), IEEE, pp 9-15

Mcintosh S, Kamei Y, Adams B, Hassan AE (2016) An empirical study of the impact of modern code review practices on software quality. Empir Softw Eng 21(5):2146–2189. https://doi.org/10.1007/s10664-015-9381-9CrossRef

Menzies T (2020) Scott Knot with nonparametric effect size and significance test. https://gist.github.com/timm/41b3a8790c1adce26d63c5874fbea393, [Online; accessed 04-May-2021]

Menzies T, Greenwald J, Frank A (2007) Data mining static code attributes to learn defect predictors. IEEE Trans Softw Eng 33(1):2–13. https://doi.org/10.1109/TSE.2007.256941CrossRef

Midi H, Sarkar S, Rana S (2013) Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics 13:253–267. https://doi.org/10.1080/09720502.2010.10700699CrossRefMATH

Molnar C (2020) Interpretable machine learning. Lulu

Nishida K, Yamauchi K (2007) Detecting concept drift using statistical testing. In: International conference on discovery science, Springer, pp 264–269

Oliva GA, Hassan AE (2021) The gas triangle and its challenges to the development of blockchain-powered applications. In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 1463–1466

Oliva GA, Hassan AE, Jiang ZMJ (2020) An exploratory study of smart contracts in the ethereum blockchain platform. Empir Softw Eng 25(3):1864–1904CrossRef

Oliveira VC, Almeida Valadares J, A Sousa JE, Borges Vieira A, Bernardino HS, Moraes Villela S, Dias Goncalves G (2021) Analyzing transaction confirmation in ethereum using machine learning techniques. SIGMETRICS Perform Eval Rev 48(4):12–15.

Pacheco M, Oliva GA, Rajbahadur GK, Hassan AE (2022) Is my transaction done yet? an empirical study of transaction processing times in the ethereum blockchain platform. ACM Transactions on Software Engineering and Methodology

Pierro GA, Rocha H (2019) The influence factors on ethereum transaction fees. In: 2019 IEEE/ACM 2Nd international workshop on emerging trends in software engineering for blockchain, WETSEB, IEEE. pp 24-31

Pierro GA, Rocha H, Ducasse S, Marchesi M, Tonelli R (2022) A user-oriented model for oracles’ gas price prediction. Futur Gener Comput Syst 128:142–157CrossRef

Rajbahadur GK, Wang S, Ansaldi G, Kamei Y, Hassan AE (2021) The impact of feature importance methods on the interpretation of defect classifiers. IEEE Transactions on Software Engineering

Ribeiro MT, Singh S, Guestrin C (2016) “why should i trust you?”: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA, KDD ’16, pp 1135–1144, https://doi.org/10.1145/2939672.2939778

Romano J, Kromrey J, Coraggio J, Skowronek J (2006) Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen’sd for evaluating group differences on the NSSE and other surveys?. In: Annual meeting of the Florida Association of Institutional Research, pp 1–3

Scott AJ, Knott M (1974) A cluster analysis method for grouping means in the analysis of variance. Biometrics 30(3):507–512CrossRefMATH

Signer C (2018) Gas cost analysis for ethereum smart contracts. Master’s thesis, ETH Zurich, Department of Computer Science

Singh HJ, Hafid AS (2020) Prediction of transaction confirmation time in ethereum blockchain using machine learning. In: Prieto J, Das AK, Ferretti S, Pinto A, Corchado JM (eds) Blockchain and applications. Springer International Publishing, Cham, pp 126–133

Tagra A, Zhang H, Rajbahadur GK, Hassan AE (2022) Revisiting reopened bugs in open source software systems. Empir Softw Eng 27(4):1–34CrossRef

Tantithamthavorn C, Hassan AE (2018) An experience report on defect modelling in practice: Pitfalls and challenges. In: Proceedings of the 40th International conference on software engineering: Software engineering in practice, pp 286–295

Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) An empirical comparison of model validation techniques for defect prediction models. IEEE Trans Softw Eng 43(1):1–18CrossRef

Tantithamthavorn C, Hassan AE, Matsumoto K (2020) The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Trans Software Eng 46(11):1200–1219. https://doi.org/10.1109/TSE.2018.2876537CrossRef

Tantithamthavorn CK, Jiarpakdee J (2021) Explainable ai for software engineering. In: 2021 36Th IEEE/ACM international conference on automated software engineering, ASE, IEEE, pp. 1-2

Thongtanunam P, Hassan AE (2020) Review dynamics and their impact on software quality. IEEE Trans Softw Eng 47(12):2698–2712CrossRef

Viggiato M, Bezemer CP (2020) Trouncing in dota 2: an investigation of blowout matches. In: Proceedings of the AAAI conference on artificial intelligence and interactive digital entertainment, vol 16, pp 294–300

Werner SM, Pritz PJ, Perez D (2020) Step on the gas? a better approach for recommending the ethereum gas price. In: Mathematical Research for Blockchain Economy, Springer, pp 161–177

Wood G (2019) Ethereum: A secure decentralised generalised transaction ledger byzantium version 7e819ec - 2019-10-20. https://ethereum.github.io/yellowpaper/paper.pdf

Yatish S, Jiarpakdee J, Thongtanunam P, Tantithamthavorn C (2019) Mining software defects: Should we consider affected releases?. In: Proceedings of the 41st international conference on software engineering, IEEE Press, ICSE ’19, pp 654–665, https://doi.org/10.1109/ICSE.2019.00075

Zarir AA, Oliva GA, Jiang ZM, Hassan AE (2021) Developing cost-effective blockchain-powered applications: a case study of the gas usage of smart contract transactions in the ethereum blockchain platform. ACM Transactions on Software Engineering and Methodology (TOSEM) 30(3):1–38CrossRef

Zhang H, Wang S, Chen TH, Zou Y, Hassan AE (2019) An empirical study of obsolete answers on stack overflow. IEEE Trans Softw Eng 47 (4):850–862CrossRef

Zou W, Lo D, Kochhar PS, Le XBD, Xia X, Feng Y, Chen Z, Xu B (2019) Smart contract development: Challenges and opportunities. IEEE Trans Softw Eng 47(10):2084–2106

Title: What makes Ethereum blockchain transactions be processed fast or slow? An empirical study
Authors: Michael Pacheco
Gustavo A. Oliva
Gopi Krishnan Rajbahadur
Ahmed E. Hassan
Publication date: 01-03-2023
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 2/2023
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-022-10283-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2023

Towards understanding quality challenges of the federated learning for neural networks: a first look from the lens of robustness

An interview study about the use of logs in embedded software engineering

An empirical study of text-based machine learning models for vulnerability detection

Semantically-enhanced topic recommendation systems for software projects

Android decompiler performance on benign and malicious apps: an empirical study

Using acceptance tests to predict merge conflict risk

Premium Partner